The goal of this project is to integrate several demonstrated technologies for restoring lower limb function after paralysis from spinal cord injury (SCI), and examine novel stimulation schemes designed to minimize fatigue of the knee extensor musculature during standing. All existing stimulation systems for standing after SCI rely on continuous activation of the knee extensors, which results in rapid fatigue that limits their functionality and clinical utility. The primary objective of this translational study is to improve the performance of neuroprostheses for standing by developing and implementing advanced stimulation paradigms that exploit the selectivity of multi- contact peripheral nerve electrodes to prolong standing duration. Strategie for exploiting the selective activation of multiple synergistic muscle fiber populations include alternating between independent groups to reduce stimulus duty cycle, interleaving stimulus pulses to reduce local stimulus frequency, and a previously unexplored paradigm in which the oscillations of sinusoidal forces generated by each population produce a constant net output that exceeds any individual contribution. There has yet to be a successful critical evaluation of any of these methods chronically in humans, which still require rigorous bench testing. Stable and selective peripheral nerve interfaces (multi-contact spiral cuff electrodes) have recently become available for chronic human implantation and will enable the clinical assessment of advanced stimulation paradigms in individuals with SCI. We will implement each paradigm in recipients of implanted standing neuroprostheses and determine their relative benefits in terms of knee extension moment, endurance, robustness and elapsed standing duration. The numerous parameters that need to be adjusted for each muscle fiber population are currently selected ad hoc in a time consuming trial-and-error process. We will perform a series of chronic animal studies to develop and test automated methods for tuning each stimulation paradigm and selecting optimal parameters to maximize performance and generalize them to other neural interface technologies. The resulting tuning and optimization methods resulting from these studies will be verified clinically with users of a variety of implanted neuroprostheses, and will ultimately be suitable for transfer to other clinical applications.